fix flip_aug, alpha_mask, random_crop issue in caching in caching strategy

library/train_util.py

@@ -993,9 +993,26 @@ class BaseDataset(torch.utils.data.Dataset):
         # sort by resolution
         image_infos.sort(key=lambda info: info.bucket_reso[0] * info.bucket_reso[1])
 
-        # split by resolution
-        batches = []
-        batch = []
+        # split by resolution and some conditions
+        class Condition:
+            def __init__(self, reso, flip_aug, alpha_mask, random_crop):
+                self.reso = reso
+                self.flip_aug = flip_aug
+                self.alpha_mask = alpha_mask
+                self.random_crop = random_crop
+
+            def __eq__(self, other):
+                return (
+                    self.reso == other.reso
+                    and self.flip_aug == other.flip_aug
+                    and self.alpha_mask == other.alpha_mask
+                    and self.random_crop == other.random_crop
+                )
+
+        batches: List[Tuple[Condition, List[ImageInfo]]] = []
+        batch: List[ImageInfo] = []
+        current_condition = None
+
         logger.info("checking cache validity...")
         for info in tqdm(image_infos):
             subset = self.image_to_subset[info.image_key]
@@ -1016,20 +1033,23 @@ class BaseDataset(torch.utils.data.Dataset):
                 if cache_available:  # do not add to batch
                     continue
 
-            # if last member of batch has different resolution, flush the batch
-            if len(batch) > 0 and batch[-1].bucket_reso != info.bucket_reso:
-                batches.append(batch)
+            # if batch is not empty and condition is changed, flush the batch. Note that current_condition is not None if batch is not empty
+            condition = Condition(info.bucket_reso, subset.flip_aug, subset.alpha_mask, subset.random_crop)
+            if len(batch) > 0 and current_condition != condition:
+                batches.append((current_condition, batch))
                 batch = []
 
             batch.append(info)
+            current_condition = condition
 
             # if number of data in batch is enough, flush the batch
             if len(batch) >= caching_strategy.batch_size:
-                batches.append(batch)
+                batches.append((current_condition, batch))
                 batch = []
+                current_condition = None
 
         if len(batch) > 0:
-            batches.append(batch)
+            batches.append((current_condition, batch))
 
         # if cache to disk, don't cache latents in non-main process, set to info only
         if caching_strategy.cache_to_disk and not is_main_process:
@@ -1041,9 +1061,8 @@ class BaseDataset(torch.utils.data.Dataset):
 
         # iterate batches: batch doesn't have image here. image will be loaded in cache_batch_latents and discarded
         logger.info("caching latents...")
-        for batch in tqdm(batches, smoothing=1, total=len(batches)):
-            # cache_batch_latents(vae, cache_to_disk, batch, subset.flip_aug, subset.alpha_mask, subset.random_crop)
-            caching_strategy.cache_batch_latents(model, batch, subset.flip_aug, subset.alpha_mask, subset.random_crop)
+        for condition, batch in tqdm(batches, smoothing=1, total=len(batches)):
+            caching_strategy.cache_batch_latents(model, batch, condition.flip_aug, condition.alpha_mask, condition.random_crop)
 
     def cache_latents(self, vae, vae_batch_size=1, cache_to_disk=False, is_main_process=True, file_suffix=".npz"):
         # マルチGPUには対応していないので、そちらはtools/cache_latents.pyを使うこと